Abstract: |
A variety of techniques are used automate the collection and classification of workout data gathered by a wearable physiological monitor. The classification process is staged in order to correctly and efficiently characterize a workout type. Initially, a generalized workout event is detected using motion and heart rate data. Then a location of the monitor on a user is determined. An artificial intelligence engine can then be conditionally applied (if a workout is occurring and a suitable device location is detected) to identify the type of workout. In addition to improved speed and accuracy, a workout detection process implemented in this manner can be realized with a sufficiently small computational footprint for deployment on a wearable physiological monitor. |
Inventor: |
Todd, Brian Anthony (Boston, MA, US); Capodilupo, John Vincenzo (Boston, MA, US); Capodilupo, Emily Rachel (Boston, MA, US); Ahmed, William (Boston, MA, US) |
Applicant: |
Whoop, Inc. (Boston, MA, US) |
Face Assignee: |
N/A |
Filed: |
2020-01-31 |
Issued: |
2020-07-30 |
Claims: |
21 |
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US20200237262
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21. A computer program product comprising computer executable code embodied in a non-transitory computer-readable medium that, when executing on a wearable physiological monitor, performs the steps of:
(7)
(6)
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30. A system comprising:
(6)
(4)
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